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- Author or Editor: D. Rocchini x
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Litter mass represents a key factor in the process of carbon sequestration. Pine plantations are known to accumulate high amounts of litter, which may act as real carbon sink only if it persists for long time. Thus, predicting litter mass by means of robust and straightforward models which convey information from several ecological predictors become crucial in this framework. The aim of this paper was to test for relationships between environmental predictors and pine litter mass (total branch, needle and cone) by Generalized Linear Models, exploring the contribution of different environmental variables in describing patterns of pine litter mass. Different predictors accounting for seasonality, spatial and geomorphological variability, pine stand properties, remotely sensed derived biomass were taken into account. Considering total litter mass, observed vs. predicted values showed a statistically highly significant relation (p < 0.001) by retaining four variables: elevation, latitude, stand age and season. Similar results were achieved for the needle litter mass, which represented anyway the largest fraction of the litter. Regarding branch litter mass, only stand age appeared to be a significant variable. For cone litter mass, no variables were statistically significant in explaining its variance. Potential ecological background processes responsible for the correlations between variables are discussed.
Rarefaction has long represented a powerful tool for detecting species richness and its variation across spatial scales. Some authors recently reintroduced the mathematical expression for calculating sample-based rarefaction curves. While some of them did not claim any advances, others presented this formula as a new analytical solution. We provide evidence about formulations of the sample-based rarefaction formula older than those recently proposed in ecological literature.
Species rarefaction curves have long been used for estimating the expected number of species as a function of sampling effort and they represent a powerful tool for quantifying the diversity of an area from local (α-diversity) to regional scale (β- and γ-diversity). Nonetheless, sampling species based on standard plant inventories represents a cost expensive approach. In this view, remotely sensed information may be straightforwardly used for predicting species rich sites. In this paper, we present spectral rarefaction, i.e., the rarefaction of reflectance values derived from satellite imagery, as an effective manner for predicting bio-diverse sites. We tested this approach in ten biogeographical subregions in Switzerland. Plant species data were derived from the Swiss ‘Biodiversity Monitoring’ programme (BDM), which represents species richness of Switzerland at the landscape scale by a systematic sample of 520 quadrats of 1 km × 1 km. Seven Landsat ETM+ images covering the whole study area were acquired. Species and spectral rarefaction were built and results were compared by Pearson correlation coefficient considering several sampling efforts (as measured by the number of sampled quadrats). Local α-diversity showed a similar pattern considering the ten biogeographical subregions while β- and γ-diversity showed higher values for regions in the Alpine arc and lower values for plateau regions and Jura mountains on the strength of the higher ecological (and spectral) variability of the former areas. Meanwhile, positive correlations between species and spectral richness values were significant only after a certain amount of area was accumulated, thus indicating a scale dependence of the fit of satellite and species data. With this paper, we introduce spectral rarefaction as an effective tool in quantifying diversity at a range of spatial scales. Obviously, the achieved results should be viewed as an aid to plan field survey rather than to replace it. We propose to use worldwide available remotely sensed information as a driver for field sampling design strategies.
Even if the establishment of nature reserves is to date a reality and the increase of protected areas is going to grow year after year, monitoring programs aiming to assess the effectiveness of the established protected areas for biodiversity conservation are still needed. That is the case for the Natura 2000 network in Europe, for which monitoring methods and programs are not yet well-established. A probabilistic sampling procedure is proposed and tested for quantifying and monitoring plant species diversity within a local network of protected areas, namely the Natura 2000 network in the Siena Province, Italy. On the basis of a sampling strategy of one 100 m 2 plot randomly located in each 1 km × 1 km cell, four Sites of Community Importance (SCIs) were investigated in 2005. The gradients in species composition at the plot scale were largely related to elevation and forest cover. The species richness values of the four SCIs were compared by means of sample-based rarefaction curves. Then, additive partitioning of species richness was applied to determine the most important spatial components in determining the total species richness of the network. Compositional differences among the plots within each SCI were the most responsible of the total species richness. These methodologies can be adopted for assessing plant species richness within a large region or within a reserve network and, if combined with additive partitioning, they can be used as a set of large scale indicators of species diversity.
A multi-stage cluster sampling is proposed for quantifying and monitoring plant species richness at multiple spatial grains over large spatial extents. An unbiased estimator of average species richness at different grains and a conservative estimator of its sampling variance are obtained in a complete design-based framework, i.e., avoiding any assumption about the ecological community under study. An application to the Nature Reserve “ Lago di Montepulciano ” demonstrates that the proposed strategy may accomplish practical advantages and quite satisfactory levels of accuracy.
Due to the difficulties of field-based species data collection at wide spatial scales, remotely sensed spectral diversity has been advocated as one of the most effective proxies of ecosystem and species diversity. It is widely accepted that the relationship between species and spectral diversity is scale dependent. However, few studies have evaluated the impacts of scale on species diversity estimates from remote sensing data. In this paper we tested the species versus spectral relationship over very large scales (extents) with a varying spatial grain using floristic data of North America. Spectral diversity explained a low amount of variance while spatial extent of the sampling units (floras) explained a high amount of variance based on results from our variance partitioning analyses. This leads to the conclusion that spectral diversity must be carefully related to species diversity, explicitly taking into account potential area effects.
Similarity in species composition among different areas plays an essential task in biodiversity management and conservation since it allows the identification of those environmental gradients that functionally operate in determining variation in species composition across spatial scale. The decay of compositional similarity with increasing spatial or environmental distance derives from: 1) the presence of spatial constraints which create a physical separation among habitats, or 2) the decrease in environmental similarity with increasing distance. Even if the distance decay of compositional similarity represents a well known pattern characterising all types of biological communities, few attempts were made to examine this pattern at small spatial scales with respect to both grain and extent. Aim of this work was to test whether the distance decay of similarity 1) can be observed at a local scale in situations where environmental conditions are relatively homogeneous and ecological barriers are absent, and 2) is dependent on the grain size at which plant community data are recorded. We selected two urban brownfields located at Bremen university campus, Germany, of 40 m × 20 m each, systematically divided in nested plots with an increasing spatial scale of 0.25 m2, 1 m2, 4 m2 and 16 m2. Both plant species composition and soil variables were recorded in each cell. Linear and logarithmic least squares regression models were applied in order to examine the decay of similarity due to spatial distance (calculated as the Euclidean distance among pairs of plots) and environmental distance (calculated as the Euclidean distance among PCA-transformed soil variables). A general lack of distance decay was observed, irrespective of the type of distance (spatial or environmental) or the grain size. We argue that this is probably due to a random variation both of the important environmental parameters and of the local distribution patterns of individual species, the latter mainly caused by the high dispersal abilities of the majority of species occurring in the brownfields.
Rarefaction is a widely applied technique for comparing the species richness of samples that differ in area, volume or sampling effort. Despite widespread adoption of sample-based rarefaction curves, serious concerns persist. In this paper, we address the issue of the spatial arrangement of sampling units when computing sample-based rarefaction curves. If the spatial arrangement is neglected when building rarefaction curves, a direct comparison of species richness estimates obtained for areas that differ in their spatial extent is not possible, even if they were sampled with a similar intensity. We demonstrate a major effect of the spatial extent of the samples on species richness estimates through the use of data from a temperate forest. We show that the use of Spatially Constrained Rarefaction (SCR) results in species richness estimates that are directly comparable for areas that differ in spatial extent. As expected, standard rarefaction curves tend to overestimate species richness because they ignore the spatial autocorrelation of species composition among sampling units. This spatial autocorrelation is captured by the SCR, thus providing a useful technique for characterizing the spatial structure of biodiversity patterns. Further work is necessary to determine how species richness estimates and the shape of the SCR are affected by the method of spatial constraint and sampling unit density and distribution.
Invasion by alien plant species may be rapid and aggressive, causing erosion of local biodiversity. This is particularly true for islands, where natural and anthropogenic corridors promote the rapid spread of invasive plants. Although evidence shows that corridors may facilitate plant invasions, the question of how their importance in the spread of alien species varies along environmental gradients deserves more attention. Here, we addressed this issue by examining diversity patterns (species richness of endemic, native and alien species) along and across roads, along an elevation gradient from sea-level up to 2050 m a.s.l. in Tenerife (Canary Islands, Spain), at multiple spatial scales. Species richness was assessed using a multi-scale sampling design consisting of 59 T-transects of 150 m × 2 m, along three major roads each placed over the whole elevation gradient. Each transect was composed of three sections of five plots each: Section 1 was located on the road edges, Section 2 at intermediate distance, and Section 3 far from the road edge, the latter representing the “native community” less affected by road-specific disturbance. The effect of elevation and distance from roadsides was evaluated for the three groups of species (endemic, native and alien species), using parametric and non-parametric regression analyses as well as additive diversity partitioning. Differences among roads explained the majority of the variation in alien species richness and composition. Patterns in alien species richness were also affected by elevation, with a decline in richness with increasing elevation and no alien species recorded at high elevations. Elevation was the most important factor determining patterns in endemic and native species. These findings confirm that climate filtering reflected in varying patterns along elevational gradients is an important determinant of the richness of alien species (which are not adapted to high elevations), while anthropogenic pressures may explain the richness of alien species at low elevation.
